Stack arrays in sequence depth wise (along third axis).
This is equivalent to concatenation along the third axis after 2-D arrays
of shape (M,N) have been reshaped to (M,N,1) and 1-D arrays of shape
(N,) have been reshaped to (1,N,1). Rebuilds arrays divided by
This function makes most sense for arrays with up to 3 dimensions. For
instance, for pixel-data with a height (first axis), width (second axis),
and r/g/b channels (third axis). The functions concatenate, stack and
block provide more general stacking and concatenation operations.
The arrays must have the same shape along all but the third axis.
1-D or 2-D arrays must have the same shape.
The array formed by stacking the given arrays, will be at least 3-D.
Join a sequence of arrays along an existing axis.
Join a sequence of arrays along a new axis.
Assemble an nd-array from nested lists of blocks.
Stack arrays in sequence vertically (row wise).
Stack arrays in sequence horizontally (column wise).
Stack 1-D arrays as columns into a 2-D array.
Split array along third axis.
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> a = np.array([,,])
>>> b = np.array([,,])